COSINE: A web server for clonal and subclonal structure inference and evolution in cancer genomics

[...]these methods had different programming languages and data input formats, which limited their use and comparison. [...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstructi...

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Veröffentlicht in:Dōngwùxué yánjiū 2022-01, Vol.43 (1), p.75-77
Hauptverfasser: Yuan, Xi-Guo, Zhao, Yuan, Guo, Yang, Ge, Lin-Mei, Liu, Wei, Wen, Shi-Yu, Li, Qi, Wan, Zhang-Bo, Zheng, Pei-Na, Guo, Tao, Li, Zhi-Da, Peifer, Martin, Cun, Yu-Peng
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container_end_page 77
container_issue 1
container_start_page 75
container_title Dōngwùxué yánjiū
container_volume 43
creator Yuan, Xi-Guo
Zhao, Yuan
Guo, Yang
Ge, Lin-Mei
Liu, Wei
Wen, Shi-Yu
Li, Qi
Wan, Zhang-Bo
Zheng, Pei-Na
Guo, Tao
Li, Zhi-Da
Peifer, Martin
Cun, Yu-Peng
description [...]these methods had different programming languages and data input formats, which limited their use and comparison. [...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstruction methods. Generally, subclonal reconstruction involves three steps: first, calculate the fraction of variant alleles of somatic mutations with relevant copy number changes and tumor purity; second, calculate the cancer cell fraction (CCF) in the tumor (using structural variation information correction); third, cluster the CCFs to identify subclonal structures and construct related phylogenetic trees. [...]the accuracy and resolution of each subclonal inference method depends on the experimental design and mutation characteristics of the specific tumor being reconstructed. Figure 1A, B show the general workflow for the inference of clonal and subclonal structure, which includes five steps: (1) somatic mutation calling from matched normal-tumor tissue samples based on next-generation sequencing (NGS) data; (2) gene copy number calling using NGS data; (3) CCF estimation; (4) clonal and subclonal structure inference via CCF clustering; and (5) clonal and subclonal evolutionary tree construction. Xi-Guo Yuan1,#, Yuan Zhao1#, Yang Guo1, Lin-Mei Ge2, Wei Liu3, Shi-Yu Wen3, Qi Li1, Zhang-Bo Wan1, Pei-Na Zheng1, Tao Guo3, Zhi-Da Li3, Martin Peifer4, Yu-Peng Cun5,2· 1 School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China 2 iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China 3 Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China 4 Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany 5 Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children's Hospital of Chongqing Medical University, Chongqing 400014, China #Authors contributed equally to this work *Corresponding author, E-mail: cunyp@cqmu.edu.cn This is an open-access article distributed und
doi_str_mv 10.24272/j.issn.2095-8137.2021.250
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[...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstruction methods. Generally, subclonal reconstruction involves three steps: first, calculate the fraction of variant alleles of somatic mutations with relevant copy number changes and tumor purity; second, calculate the cancer cell fraction (CCF) in the tumor (using structural variation information correction); third, cluster the CCFs to identify subclonal structures and construct related phylogenetic trees. [...]the accuracy and resolution of each subclonal inference method depends on the experimental design and mutation characteristics of the specific tumor being reconstructed. Figure 1A, B show the general workflow for the inference of clonal and subclonal structure, which includes five steps: (1) somatic mutation calling from matched normal-tumor tissue samples based on next-generation sequencing (NGS) data; (2) gene copy number calling using NGS data; (3) CCF estimation; (4) clonal and subclonal structure inference via CCF clustering; and (5) clonal and subclonal evolutionary tree construction. Xi-Guo Yuan1,#, Yuan Zhao1#, Yang Guo1, Lin-Mei Ge2, Wei Liu3, Shi-Yu Wen3, Qi Li1, Zhang-Bo Wan1, Pei-Na Zheng1, Tao Guo3, Zhi-Da Li3, Martin Peifer4, Yu-Peng Cun5,2· 1 School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China 2 iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China 3 Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China 4 Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany 5 Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children's Hospital of Chongqing Medical University, Chongqing 400014, China #Authors contributed equally to this work *Corresponding author, E-mail: cunyp@cqmu.edu.cn This is an open-access article distributed under the terms of the</description><identifier>ISSN: 2095-8137</identifier><identifier>ISSN: 0254-5853</identifier><identifier>DOI: 10.24272/j.issn.2095-8137.2021.250</identifier><identifier>PMID: 34845880</identifier><language>eng</language><publisher>China: Kunming Institute of Zoology, The Chinese Academy of Sciences</publisher><subject>Alleles ; Bioinformatics ; Botany ; Cancer ; Child development ; Clustering ; Cognitive ability ; Colleges &amp; universities ; Copy number ; Design of experiments ; Disorders ; Educational institutions ; Evolution ; Experimental design ; Genomes ; Genomics ; Germplasm ; Inference ; Information technology ; Internet ; Laboratories ; Letter to the Editor ; Linux ; Mathematical analysis ; Medical research ; Medical sciences ; Methods ; Mutation ; Neoplasms ; Neoplasms - genetics ; Next-generation sequencing ; Pediatrics ; Phylogenetics ; Phylogeny ; Programming languages ; R&amp;D ; Reconstruction ; Research &amp; development ; Research facilities ; Servers ; Software ; Technology ; Tumors ; Workflow</subject><ispartof>Dōngwùxué yánjiū, 2022-01, Vol.43 (1), p.75-77</ispartof><rights>Copyright Kunming Institute of Zoology, The Chinese Academy of Sciences Jan 2022</rights><rights>Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences 2021 Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c454t-fdd8d73a9e4415804851339542190449e686a59dee726d72db03fa7fae4c27cb3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34845880$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yuan, Xi-Guo</creatorcontrib><creatorcontrib>Zhao, Yuan</creatorcontrib><creatorcontrib>Guo, Yang</creatorcontrib><creatorcontrib>Ge, Lin-Mei</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Wen, Shi-Yu</creatorcontrib><creatorcontrib>Li, Qi</creatorcontrib><creatorcontrib>Wan, Zhang-Bo</creatorcontrib><creatorcontrib>Zheng, Pei-Na</creatorcontrib><creatorcontrib>Guo, Tao</creatorcontrib><creatorcontrib>Li, Zhi-Da</creatorcontrib><creatorcontrib>Peifer, Martin</creatorcontrib><creatorcontrib>Cun, Yu-Peng</creatorcontrib><creatorcontrib>Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children’s Hospital of Chongqing Medical University, Chongqing 400014, China</creatorcontrib><creatorcontrib>西安电子科技大学计算科学和技术学院, 陕西 西安 710071, 中国</creatorcontrib><creatorcontrib>iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China</creatorcontrib><creatorcontrib>中国科学院昆明植物所西南野生生物资源种质库, iFlora生物信息学中心, 云南 昆明 650201, 中国</creatorcontrib><creatorcontrib>School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China</creatorcontrib><creatorcontrib>重庆医科大学附属儿童医院儿科研究所认知发育与学习记忆障碍转化医学重庆市重点实验室, 国家儿童健康与疾病临床医学研究中心, 重庆400014, 中国</creatorcontrib><creatorcontrib>Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China</creatorcontrib><creatorcontrib>玉溪融建信息技术有限公司, 云南 玉溪 653100, 中国</creatorcontrib><creatorcontrib>Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany</creatorcontrib><title>COSINE: A web server for clonal and subclonal structure inference and evolution in cancer genomics</title><title>Dōngwùxué yánjiū</title><addtitle>Zool Res</addtitle><description>[...]these methods had different programming languages and data input formats, which limited their use and comparison. [...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstruction methods. Generally, subclonal reconstruction involves three steps: first, calculate the fraction of variant alleles of somatic mutations with relevant copy number changes and tumor purity; second, calculate the cancer cell fraction (CCF) in the tumor (using structural variation information correction); third, cluster the CCFs to identify subclonal structures and construct related phylogenetic trees. [...]the accuracy and resolution of each subclonal inference method depends on the experimental design and mutation characteristics of the specific tumor being reconstructed. Figure 1A, B show the general workflow for the inference of clonal and subclonal structure, which includes five steps: (1) somatic mutation calling from matched normal-tumor tissue samples based on next-generation sequencing (NGS) data; (2) gene copy number calling using NGS data; (3) CCF estimation; (4) clonal and subclonal structure inference via CCF clustering; and (5) clonal and subclonal evolutionary tree construction. Xi-Guo Yuan1,#, Yuan Zhao1#, Yang Guo1, Lin-Mei Ge2, Wei Liu3, Shi-Yu Wen3, Qi Li1, Zhang-Bo Wan1, Pei-Na Zheng1, Tao Guo3, Zhi-Da Li3, Martin Peifer4, Yu-Peng Cun5,2· 1 School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China 2 iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China 3 Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China 4 Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany 5 Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children's Hospital of Chongqing Medical University, Chongqing 400014, China #Authors contributed equally to this work *Corresponding author, E-mail: cunyp@cqmu.edu.cn This is an open-access article distributed under the terms of the</description><subject>Alleles</subject><subject>Bioinformatics</subject><subject>Botany</subject><subject>Cancer</subject><subject>Child development</subject><subject>Clustering</subject><subject>Cognitive ability</subject><subject>Colleges &amp; universities</subject><subject>Copy number</subject><subject>Design of experiments</subject><subject>Disorders</subject><subject>Educational institutions</subject><subject>Evolution</subject><subject>Experimental design</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Germplasm</subject><subject>Inference</subject><subject>Information technology</subject><subject>Internet</subject><subject>Laboratories</subject><subject>Letter to the Editor</subject><subject>Linux</subject><subject>Mathematical analysis</subject><subject>Medical research</subject><subject>Medical sciences</subject><subject>Methods</subject><subject>Mutation</subject><subject>Neoplasms</subject><subject>Neoplasms - genetics</subject><subject>Next-generation sequencing</subject><subject>Pediatrics</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Programming languages</subject><subject>R&amp;D</subject><subject>Reconstruction</subject><subject>Research &amp; development</subject><subject>Research facilities</subject><subject>Servers</subject><subject>Software</subject><subject>Technology</subject><subject>Tumors</subject><subject>Workflow</subject><issn>2095-8137</issn><issn>0254-5853</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNpdkUtvGyEUhVm0iqM0f6FC7aYbuzwHxotKkZWXFCWLpGvEMHdSrDG4MOOq_z6M41hpV1zOPffowofQF0oWTDDFvq8XPuewYKSWc025KhWjCybJB3R6FGfoPGffEFnq0q5O0IwLLaTW5BQ1q4fH2_vLJb7Af6DBGdIOEu5iwq6PwfbYhhbnsTnc8pBGN4wJsA8dJAgO9g7YxX4cfAxFx84WOeFnCHHjXf6EPna2z3B-OM_Qz6vLp9XN_O7h-nZ1cTd3Qoph3rWtbhW3NQhBpSZCl4V5LQWjNRGihkpXVtYtgGJVq1jbEN5Z1VkQjinX8DP04zV3OzYbaB2EIdnebJPf2PTXROvNv53gf5nnuDNaCc6EKgHfDgEp_h4hD2bjs4O-twHimA2rylKcMiaL9et_1nUcU_mhySUl1ZSqKXD56nIp5pygOy5DidkTNGszETQTLDPBMhNBUwiW4c_vn3McfWPHXwD3k5uk</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Yuan, Xi-Guo</creator><creator>Zhao, Yuan</creator><creator>Guo, Yang</creator><creator>Ge, Lin-Mei</creator><creator>Liu, Wei</creator><creator>Wen, Shi-Yu</creator><creator>Li, Qi</creator><creator>Wan, Zhang-Bo</creator><creator>Zheng, Pei-Na</creator><creator>Guo, Tao</creator><creator>Li, Zhi-Da</creator><creator>Peifer, Martin</creator><creator>Cun, Yu-Peng</creator><general>Kunming Institute of Zoology, The Chinese Academy of Sciences</general><general>Science Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H95</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L.G</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220101</creationdate><title>COSINE: A web server for clonal and subclonal structure inference and evolution in cancer genomics</title><author>Yuan, Xi-Guo ; Zhao, Yuan ; Guo, Yang ; Ge, Lin-Mei ; Liu, Wei ; Wen, Shi-Yu ; Li, Qi ; Wan, Zhang-Bo ; Zheng, Pei-Na ; Guo, Tao ; Li, Zhi-Da ; Peifer, Martin ; Cun, Yu-Peng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c454t-fdd8d73a9e4415804851339542190449e686a59dee726d72db03fa7fae4c27cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Alleles</topic><topic>Bioinformatics</topic><topic>Botany</topic><topic>Cancer</topic><topic>Child development</topic><topic>Clustering</topic><topic>Cognitive ability</topic><topic>Colleges &amp; universities</topic><topic>Copy number</topic><topic>Design of experiments</topic><topic>Disorders</topic><topic>Educational institutions</topic><topic>Evolution</topic><topic>Experimental design</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Germplasm</topic><topic>Inference</topic><topic>Information technology</topic><topic>Internet</topic><topic>Laboratories</topic><topic>Letter to the Editor</topic><topic>Linux</topic><topic>Mathematical analysis</topic><topic>Medical research</topic><topic>Medical sciences</topic><topic>Methods</topic><topic>Mutation</topic><topic>Neoplasms</topic><topic>Neoplasms - genetics</topic><topic>Next-generation sequencing</topic><topic>Pediatrics</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Programming languages</topic><topic>R&amp;D</topic><topic>Reconstruction</topic><topic>Research &amp; development</topic><topic>Research facilities</topic><topic>Servers</topic><topic>Software</topic><topic>Technology</topic><topic>Tumors</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Xi-Guo</creatorcontrib><creatorcontrib>Zhao, Yuan</creatorcontrib><creatorcontrib>Guo, Yang</creatorcontrib><creatorcontrib>Ge, Lin-Mei</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Wen, Shi-Yu</creatorcontrib><creatorcontrib>Li, Qi</creatorcontrib><creatorcontrib>Wan, Zhang-Bo</creatorcontrib><creatorcontrib>Zheng, Pei-Na</creatorcontrib><creatorcontrib>Guo, Tao</creatorcontrib><creatorcontrib>Li, Zhi-Da</creatorcontrib><creatorcontrib>Peifer, Martin</creatorcontrib><creatorcontrib>Cun, Yu-Peng</creatorcontrib><creatorcontrib>Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children’s Hospital of Chongqing Medical University, Chongqing 400014, China</creatorcontrib><creatorcontrib>西安电子科技大学计算科学和技术学院, 陕西 西安 710071, 中国</creatorcontrib><creatorcontrib>iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China</creatorcontrib><creatorcontrib>中国科学院昆明植物所西南野生生物资源种质库, iFlora生物信息学中心, 云南 昆明 650201, 中国</creatorcontrib><creatorcontrib>School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China</creatorcontrib><creatorcontrib>重庆医科大学附属儿童医院儿科研究所认知发育与学习记忆障碍转化医学重庆市重点实验室, 国家儿童健康与疾病临床医学研究中心, 重庆400014, 中国</creatorcontrib><creatorcontrib>Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China</creatorcontrib><creatorcontrib>玉溪融建信息技术有限公司, 云南 玉溪 653100, 中国</creatorcontrib><creatorcontrib>Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; 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Fisheries Abstracts (ASFA) 1: Biological Sciences &amp; Living Resources</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Dōngwùxué yánjiū</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Xi-Guo</au><au>Zhao, Yuan</au><au>Guo, Yang</au><au>Ge, Lin-Mei</au><au>Liu, Wei</au><au>Wen, Shi-Yu</au><au>Li, Qi</au><au>Wan, Zhang-Bo</au><au>Zheng, Pei-Na</au><au>Guo, Tao</au><au>Li, Zhi-Da</au><au>Peifer, Martin</au><au>Cun, Yu-Peng</au><aucorp>Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children’s Hospital of Chongqing Medical University, Chongqing 400014, China</aucorp><aucorp>西安电子科技大学计算科学和技术学院, 陕西 西安 710071, 中国</aucorp><aucorp>iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China</aucorp><aucorp>中国科学院昆明植物所西南野生生物资源种质库, iFlora生物信息学中心, 云南 昆明 650201, 中国</aucorp><aucorp>School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China</aucorp><aucorp>重庆医科大学附属儿童医院儿科研究所认知发育与学习记忆障碍转化医学重庆市重点实验室, 国家儿童健康与疾病临床医学研究中心, 重庆400014, 中国</aucorp><aucorp>Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China</aucorp><aucorp>玉溪融建信息技术有限公司, 云南 玉溪 653100, 中国</aucorp><aucorp>Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>COSINE: A web server for clonal and subclonal structure inference and evolution in cancer genomics</atitle><jtitle>Dōngwùxué yánjiū</jtitle><addtitle>Zool Res</addtitle><date>2022-01-01</date><risdate>2022</risdate><volume>43</volume><issue>1</issue><spage>75</spage><epage>77</epage><pages>75-77</pages><issn>2095-8137</issn><issn>0254-5853</issn><abstract>[...]these methods had different programming languages and data input formats, which limited their use and comparison. [...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstruction methods. Generally, subclonal reconstruction involves three steps: first, calculate the fraction of variant alleles of somatic mutations with relevant copy number changes and tumor purity; second, calculate the cancer cell fraction (CCF) in the tumor (using structural variation information correction); third, cluster the CCFs to identify subclonal structures and construct related phylogenetic trees. [...]the accuracy and resolution of each subclonal inference method depends on the experimental design and mutation characteristics of the specific tumor being reconstructed. Figure 1A, B show the general workflow for the inference of clonal and subclonal structure, which includes five steps: (1) somatic mutation calling from matched normal-tumor tissue samples based on next-generation sequencing (NGS) data; (2) gene copy number calling using NGS data; (3) CCF estimation; (4) clonal and subclonal structure inference via CCF clustering; and (5) clonal and subclonal evolutionary tree construction. Xi-Guo Yuan1,#, Yuan Zhao1#, Yang Guo1, Lin-Mei Ge2, Wei Liu3, Shi-Yu Wen3, Qi Li1, Zhang-Bo Wan1, Pei-Na Zheng1, Tao Guo3, Zhi-Da Li3, Martin Peifer4, Yu-Peng Cun5,2· 1 School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China 2 iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China 3 Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China 4 Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany 5 Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children's Hospital of Chongqing Medical University, Chongqing 400014, China #Authors contributed equally to this work *Corresponding author, E-mail: cunyp@cqmu.edu.cn This is an open-access article distributed under the terms of the</abstract><cop>China</cop><pub>Kunming Institute of Zoology, The Chinese Academy of Sciences</pub><pmid>34845880</pmid><doi>10.24272/j.issn.2095-8137.2021.250</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record>
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subjects Alleles
Bioinformatics
Botany
Cancer
Child development
Clustering
Cognitive ability
Colleges & universities
Copy number
Design of experiments
Disorders
Educational institutions
Evolution
Experimental design
Genomes
Genomics
Germplasm
Inference
Information technology
Internet
Laboratories
Letter to the Editor
Linux
Mathematical analysis
Medical research
Medical sciences
Methods
Mutation
Neoplasms
Neoplasms - genetics
Next-generation sequencing
Pediatrics
Phylogenetics
Phylogeny
Programming languages
R&D
Reconstruction
Research & development
Research facilities
Servers
Software
Technology
Tumors
Workflow
title COSINE: A web server for clonal and subclonal structure inference and evolution in cancer genomics
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